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ai-sage/GigaAM-Multilingual

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GigaAM Multilingual

GigaAM Multilingual is a family of Conformer-based foundation models (220M / 600M parameters) pre-trained with a HuBERT-style objective on 2M hours of speech across 70+ languages and fine-tuned for speech recognition with character-wise CTC decoders on 50K hours.

The models provide best-in-class open-source quality on Russian, Kazakh, Kyrgyz, and Uzbek, and moderate quality on English.

GigaAM Multilingual includes the following model variants:

—ssl — 220M self-supervised encoder
—ctc — 220M ASR model with a character-wise CTC decoder
—large_ssl — 600M self-supervised encoder
—large_ctc — 600M ASR model with a character-wise CTC decoder

Model Performance

Word Error Rate (%) on Common Voice (CV), FLEURS, and internal in-the-wild test sets. Utterances longer than 30 s and references containing digits are excluded; references/hypotheses are normalized (lowercasing, punctuation removal, numerals→words); greedy decoding. Best per row in bold.

Language	Dataset	GigaAM Multilingual	GigaAM Multilingual Large	Omnilingual 1B (LLM)	Seamless M4T large v2	Whisper large v3
English	CV	26.0	21.5	24.7	16.2	20.0
English	FLEURS	12.2	9.4	7.1	5.8	3.9
Russian	CV	7.1	5.1	13.6	9.2	9.1
Russian	FLEURS	4.4	3.0	6.4	4.6	3.1
Russian	Internal	7.6	6.0	14.6	16.1	10.1
Kazakh	CV	17.2	13.8	23.7	23.8	57.8
Kazakh	FLEURS	5.2	4.4	6.6	6.8	32.4
Kazakh	Internal	18.8	15.8	32.2	62.9	65.2
Kyrgyz	CV	12.5	10.2	21.6	14.3	95.2
Kyrgyz	FLEURS	7.0	5.5	8.1	9.5	86.3
Kyrgyz	Internal	11.1	9.8	25.0	78.3	102.2
Uzbek	CV	11.3	9.2	32.8	25.1	109.9
Uzbek	FLEURS	10.0	7.3	15.4	11.9	105.4
Uzbek	Internal	13.8	12.7	30.2	40.0	120.6

Usage

python

from transformers import AutoModel

revision = "ctc"  # any variant: ssl, ctc, large_ssl, large_ctc
model = AutoModel.from_pretrained(
    "ai-sage/GigaAM-Multilingual",
    revision=revision,
    trust_remote_code=True,
)

transcription = model.transcribe("example.wav")
print(transcription)

Recommended versions:

—torch==2.10.*, torchaudio==2.10.*
—transformers==5.*
—(any) hydra-core, omegaconf

Full usage guide can be found in the example.

Fine-tuning to a new language

The ssl / large_ssl backbones can be adapted to a new language — see the fine-tuning guide and the example notebook.

Citation

bibtex

@misc{gigaam_multilingual,
      title={GigaAM Multilingual: Foundation Model for Underrepresented Languages},
      author={Andrei Kuzmenko and Alexandr Maximenko and Aleksandr Kutsakov and Georgii Gospodinov and Dmitrii Bolotov and Oleg Kutuzov and Pavel Bogomolov and Fyodor Minkin},
      year={2026},
      eprint={2607.10371},
      archivePrefix={arXiv},
      primaryClass={eess.AS},
      url={https://arxiv.org/abs/2607.10371}
}